eXplainable artificial intelligence (XAI) methods have emerged to convert the black box of machine learning (ML) models into a more digestible form. These methods help to communicate how the model works with the aim of making ML models more transparent and increasing the trust of end-users into their output. SHapley Additive exPlanations (SHAP) and Local Interpretable Model Agnostic Explanation (LIME) are two widely used XAI methods, particularly with tabular data. In this perspective piece, we discuss the way the explainability metrics of these two methods are generated and propose a framework for interpretation of their outputs, highlighting their weaknesses and strengths. Specifically, we discuss their outcomes in terms of model-dependency and in the presence of collinearity among the features, relying on a case study from the biomedical domain (classification of individuals with or without myocardial infarction). The results indicate that SHAP and LIME are highly affected by the adopted ML model and feature collinearity, raising a note of caution on their usage and interpretation.

翻译：可解释人工智能（XAI）方法应运而生，旨在将机器学习（ML）模型的“黑箱”转化为更易于理解的形式。这些方法有助于阐明模型的工作原理，从而增强机器学习模型的透明度，并提升终端用户对其输出结果的信任度。SHapley可加性解释（SHAP）与局部可解释模型无关解释（LIME）是两种广泛使用的XAI方法，尤其适用于表格数据。本文通过视角性论述，探讨了这两种方法生成可解释性指标的机制，提出了一个解释其输出结果的框架，并着重分析了各自的优势与局限。具体而言，我们结合生物医学领域的案例研究（针对是否患有心肌梗死个体的分类问题），从模型依赖性和特征共线性两个维度讨论了它们的表现。研究结果表明，SHAP和LIME方法显著受所采用的机器学习模型及特征共线性的影响，这提示我们在使用和解释这些方法时需要保持审慎态度。